Nives Bogataj Jontez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nives Bogataj Jontez has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Alenka Baruca Arbeiter
Univerza na Primorskem Fakulteta za matematiko naravoslovje in informacijske tehnologije
1 shared publication - Ana Petelin
Faculty of Health Science, University of Primorska, Polje 42, 6310 Izola, Slovenia
1 shared publication - Dunja Bandelj
Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije
1 shared publication - Jure Pražnikar
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
1 shared publication - Karin Šik Novak
Faculty of Health Sciences, University of Primorska, 6310 Izola, Slovenia
1 shared publication - Matjaž Hladnik
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
1 shared publication
Latest Publications
- Open Access
Could Gut Microbiota Composition Be a Useful Indicator of a Long-Term Dietary Pattern?
Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual’s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.
Open AccessCould Gut Microbiota Composition Be a Useful Indicator of a Long-Term Dietary Pattern?
Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual’s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.